*** README File for Chow and Han (2022),
***	"Descriptive Legitimacy and International Organizations: Evidence from United Nations High Commissioner for Refugees", 
*** forthcoming in Journal of Politics.

*** This file describes an overview of the information for replicating
*** the data, analysis code, variables, and results. 
*** For any questions, please contact Wilfred Chow (wilfred.chow@hku.hk) or Enze Han (enzehan@hku.hk).


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*** LIST OF FILES              ***
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Data:
CH_main.dta: vignette experiment, main study.
CH_followup.dta: vignette experiment, followup study.
CH_pretest.dta: picture survey, pretest study.

Code:
CH_main.do produces all analysis in the main text.
CH_appendix.do produces all analysis in the appendix.

Logs:
CH_jop_main.log displays the log text for the do script for the analysis in the main text.
CH_jop_appendix.log displays the log text for the do script for the analysis in the appendix.



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*** REPLICATION INSTRUCTIONS   ***
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We conducted all analysis in Stata 14.

1) Extract all files into a directory on your computer. Run do files and note that all files for reproducing figures, tables, etc. will extract into directory.
2) If necessary, install the following packages (script for installation are in do file): coefplot, estout, tabout.
3) Run ch_main.do with ch_main.dta to replicate Figures 2-8.
4) Run ch_appendix.do with ch_pretest.dta to replicate Figure A2 in Appendix.
4) Run ch_appendix.do with ch_appendix.dta to replicate Tables A1-A8 and Figures A3-A11 in Appendix.


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*** LIST OF VARIABLES          ***
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*** Main Study
id = respondent id

panel = diversity treatment where respondents viewed 1 of 4 possible groups of pictures of unhcr panel
(1 = white-male, 2 = mixed-male, 3 = white-mixed, 4 = mixed-mixed)

outcome = 2 possible scenario outcomes of unhcr report
(1 = unhcr is not critical of the US, 2 = unhcr is critical of US)

american = 5-pt measure of respondent's answer to whether outcome was good for US citizens
(1 = strongly disagree...5 = strongly agree) 

refugee = 5-pt measure of respondent's answer to whether outcome was good for refugees
(1 = strongly disagree...5 = strongly agree) 

fair = 5-pt measure of respondent's answer to whether outcome was fair                           
(1 = strongly disagree...5 = strongly agree) 

trust = 5-pt measure of respondent's answer to whether UNHCR can be trusted
(1 = strongly disagree...5 = strongly agree) 

american_b = Binarization of 5-pt measure of respondent's answer to whether outcome was good for US citizens
(1 = strongly agree/somewhat agree, 0 = neutral / somewhat disagree / strongly disagree) 

refugee_b = Binarization of 5-pt measure of respondent's answer to whether outcome was good for refugees
(1 = strongly agree/somewhat agree, 0 = neutral / somewhat disagree / strongly disagree) 

fair_b = Binarization of 5-pt measure of respondent's answer to whether outcome was fair                           
(1 = strongly agree/somewhat agree, 0 = neutral / somewhat disagree / strongly disagree) 

trust_b = Binarization of 5-pt measure of respondent's answer to whether UNHCR can be trusted
(1 = strongly agree/somewhat agree, 0 = neutral / somewhat disagree / strongly disagree) 
               
age = respondent's age broken into ordered categories
(1 = 18-24, 2 = 25-34, 3 = 35-44, 4 = 45-54, 5 = 55-64, 6 = >65)

race = respondent's race                            
(1 = white, 2 = black, 3 = hispanic 4, = asian/other)

white = respondent's racial identification as white
(0 = not white, 1 = white)

party = respondent's partisan affiliation                         
(1 = independent, 2 = democrat, 3 = republican)
         
attention =  respondent's attention level from simple attention test
(1 = high, 0 = low)

pat1 = 5-pt scale of respondent answer on how strongly they feel if someone says something bad about America
(1 = not strongly at all ... 5 = extremely strongly)

pat2 = 4-pt scale of respondent answer on how important being an American is in defining who you are
(1 = none at all, 2 = a little, 3 = somewhat, 4 = very)

pat3 = 4-pt scale of respondent answer on how proud they are of being an American
(1 = not proud at all, 2 = a little proud, 3 = somewhat proud, 4 = very proud)

interest = 5-pt measure on how interested respondents say they are in politics
(0 = no interest...5 = very interested)

media = # of days that a respondent followed news that was not sports
(1 = 1 day ... 7 = 7 days)

edu = respondent's level of education          
(1 = high school or lower, 2 = some college 3 = college, 4 = postgraduate/professional)

female = respondent's gender
(1 = female, 0 = male)

inc = respondent's household income

imm1 = 5-pt scale on respondent answer on whether immigration should be increased?
(1 = increased a lot, 2 = increased a little, 3 = left the same, 4 = decreased a little, 5 = decreased a lot)

imm2_1 = 5-pt scale on respondent answer on whether immigration is good for the US economy
(1 = strongly agree ... 5 = strongly disagree)

imm2_2 = 5-pt scale on respondent answer on whether immigration enriches cultural life for the US
(1 = strongly agree ... 5 = strongly disagree)

imm3 = 5-pt scale on respondent answer on whether the S should accept more refugees
(1 = strongly support ... 5 = strongly oppose)

race1_1 = 5-pt scale of respondent answer on whether blacks face discrimination
(1 = a lot ... 5 = none at all)

race1_2 = 5-pt scale of respondent answer on whether whites face discrimination
(1 = a lot ... 5 = none at all)

race1_3 = 5-pt scale of respondent answer on whether women face discrimination
(1 = a lot ... 5 = none at all)

race1_4 = 5-pt scale of respondent answer on whether men face discrimination
(1 = a lot ... 5 = none at all)

race1_6 = 5-pt scale of respondent answer on whether christiansface discrimination
(1 = a lot ... 5 = none at all)

race1_8 = 5-pt scale of respondent answer on whether hispanics face discrimination
(1 = a lot ... 5 = none at all)

race2 = 5-pt scale of respondent answer on whether racism is still a problem today
(1 = strongly agree ... 5 = strongly disagree)

*** Follow-up Study
id = respondent id

panel = diversity treatment where respondents viewed 1 of 4 possible groups of pictures of unhcr panel
(1 = white-male, 2 = mixed-male, 3 = white-mixed, 4 = mixed-mixed)

outcome = 2 possible scenario outcomes of unhcr report
(1 = unhcr is not critical of the US, 2 = unhcr is critical of US)

country = 2 possible scenario labeling of unhcr picture panel distribution
(1 = with country labels, 2 = without country-labels)

american = 5-pt measure of respondent's answer to whether outcome was good for US citizens
(1 = strongly disagree...5 = strongly agree) 

refugee = 5-pt measure of respondent's answer to whether outcome was good for refugees
(1 = strongly disagree...5 = strongly agree) 

fair = 5-pt measure of respondent's answer to whether outcome was fair                           
(1 = strongly disagree...5 = strongly agree) 

trust = 5-pt measure of respondent's answer to whether UNHCR can be trusted
(1 = strongly disagree...5 = strongly agree) 
               
age = respondent's age
(1 = 18-24, 2 = 25-34, 3 = 35-44, 4 = 45-54, 5 = 55-64, 6 = >65)

race = respondent's race                            
(1 = white, 2 = black, 3 = hispanic 4, = asian/other)

party = respondent's partisan affiliation                         
(1 = independent, 2 = democrat, 3 = republican)

edu = respondent's level of education          
(1 = high school or lower, 2 = some college 3 = college, 4 = postgraduate/professional)

female = respondent's gender
(1 = female, 0 = male)


*** Pretest Study
id = respondent id

// Age variables
wm_age = average age of white-male pictures scored by respondent

bm_age = average age of black-male pictures scored by respondent

hm_age = average age of hispanic/arab-male pictures scored by respondent

am_age = average age of asian-male pictures scored by respondent

wf_age = average age of white-female pictures scored by respondent

bf_age = average age of black-female pictures scored by respondent

hf_age = average age of hispanic/arab-female pictures scored by respondent

af_age = average age of asian-male pictures scored by respondent

// Attractiveness variables
wm_attract = average attractiveness of white-male pictures scored by respondent

bm_attract = average attractiveness of black-male pictures scored by respondent

hm_attract = average attractiveness of hispanic/arab-male pictures scored by respondent

am_attract = average attractiveness of asian-male pictures scored by respondent

wf_attract = average attractiveness of white-female pictures scored by respondent

bf_attract = average attractiveness of black-female pictures scored by respondent

hf_attract = average attractiveness of hispanic/arab-female pictures scored by respondent

af_attract = average attractiveness of asian-male pictures scored by respondent

// Likeability variables
wm_like = average likeability of white-male pictures scored by respondent

bm_like = average likeability of black-male pictures scored by respondent

hm_like = average likeability of hispanic/arab-male pictures scored by respondent

am_like = average likeability of asian-male pictures scored by respondent

wf_like = average likeability of white-female pictures scored by respondent

bf_like = average likeability of black-female pictures scored by respondent

hf_like = average likeability of hispanic/arab-female pictures scored by respondent

af_like = average likeability of asian-male pictures scored by respondent

// Competence variables
wm_comp = average competence of white-male pictures scored by respondent

bm_comp = average competence of black-male pictures scored by respondent

hm_comp = average competence of hispanic/arab-male pictures scored by respondent

am_comp = average competence of asian-male pictures scored by respondent

wf_comp = average competence of white-female pictures scored by respondent

bf_comp = average competence of black-female pictures scored by respondent

hf_comp = average competence of hispanic/arab-female pictures scored by respondent

af_comp = average competence of asian-male pictures scored by respondent